Myoelectric prostheses, which rely on electromyography (EMG) signals to control joint movement, are often used to effectively treat upper-limb amputation. The control principles used in commercially available prostheses have been available for many decades and rely on an estimate of the amplitude of strategically placed electrodes coupled with simple rules to form control signals for controlling the operation of the prosthesis. Only a limited number of movements may be restored and to achieve a particular task for movement of the prosthesis, and the movements must be controlled sequentially as only one motion may be controlled at a time.
Pattern recognition has also been used to extract control signals for prosthetic limbs. However, in order to achieve optimal or near-optimal use of the pattern-recognition controlled prosthetic limb, example data related to each type of limb movement should be recorded from each patient to train, configure, and calibrate prosthesis movement. In addition to configuring or training the prosthesis prior to initial use, prosthesis users may be required to reconfigure the prosthesis to maintain performance levels. Conventional pattern recognition training systems often require additional hardware and technological capacity, which can hamper the user's capability to reconfigure the prosthesis.